Predictive modeling systems are widely used to forecast and assess risks in business activities, personal lives, and other endeavors based on time-based events. To make predictions and recommendations for future actions, such systems may process large caches of raw data, such as provided by client entities via data warehouses, databases, flat files, etc. After processing raw data, predictive systems may use models to identify potential issues, such as safety concerns and business trends (e.g., human resources, employee retention, recruiting activity, workers compensation claims, etc.). For example, based on a trucking company's provided data (e.g., time records for truck driver employees, health records, etc.) gathered over a period, a predictive modeling system may identify the likelihood that a certain driver may have an accident and/or need to file a worker's comp/disability claim. However, current predictive modeling systems may be tied to rigid, recurring periods (e.g., a four (4) week period) for receiving and processing client data. Further, predictive models may not be modified without intensive rebuilding operations during such a long period. A more streamlined solution is needed that allows for more versatile data aggregation for faster, more customized predictive information for client entities.